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Purpose: High-grade glioma surgery has evolved around the principal belief that a safe maximal tumor resection improves symptoms, quality of life, and survival. Mapping brain function has been recently improved by resting-state functional magnetic resonance imaging (rest-fMRI), a novel imaging technique that explores networks connectivity at “rest.” Methods: This prospective study analyzed 10 patients with high-grade glioma in whom rest-fMRI connectivity was assessed both in single-subject and in group analysis before and after surgery. Seed-based functional connectivity analysis was performed with CONN toolbox. Network identification focused on 8 major functional connectivity networks. A voxel-wise region of interest (ROI) to ROI correlation map to assess functional connectivity throughout the whole brain was computed from a priori seeds ROI in specific resting-state networks before and after surgical resection in each patient. Results: Reliable topography of all 8 resting-state networks was successfully identified in each participant before surgical resection. Single-subject functional connectivity analysis showed functional disconnection for dorsal attention and salience networks, whereas the language network demonstrated functional connection either in the case of left temporal glioblastoma. Functional connectivity in group analysis showed wide variations of functional connectivity in the default mode, salience, and sensorimotor networks. However, salience and language networks, salience and default mode networks, and salience and sensorimotor networks showed a significant correlation (P uncorrected

This research put forward an efficacious real-time deep learning-based technique to automate the process of monitoring the social distancing in transportation systems (e.g., bus stops, railway stations, airport terminals, etc.) and other public spaces with the purpose to mitigate the impact of coronavirus pandemic. The proposed technique makes use of the YOLOv3 model to segregate humans from the background of each image of a surveillance video and the linear Kalman filter for tracking the humans’ motion even in case in which another object or person overlaps the trajectory of the person under analysis. The performance of the model in human detection is extremely high as demonstrated by the accuracy of the model that reaches values higher than 95%. The detection algorithm can be applied for alerting people to keep a safe distance from each other when they are in crowded places or in groups.

Purpose Hepatic encephalopathy (HE) is a potential complication of cirrhosis. Magnetic resonance imaging (MRI) may demonstrate hyperintense T1 signal in the globi pallidi. The purpose of this study was to evaluate the performance of MRI-based radiomic features for diagnosing and grading chronic HE in adult patients affected by cirrhosis. Methods Adult patients with and without cirrhosis underwent brain MRI with identical imaging protocol on a 3T scanner. Patients without history of chronic liver disease were the control population. HE grading was based on underlying liver disease, severity of clinical manifestation, and number of encephalopathic episodes. Texture analysis was performed on axial T1-weighted images on bilateral lentiform nuclei at the level of the foramina of Monro. Diagnostic performance of texture analysis for the diagnosis and grading of HE was assessed by calculating the area under the receiver operating characteristics (AUROC) with 95% confidence interval (CI). Results The final study population consisted of 124 patients, 70 cirrhotic patients, and 54 non-cirrhotic controls. Thirty-eight patients had history of HE with 22 having an HE grade > 1. The radiomic features predicted the presence of HE with an AUROC of 0.82 (95% CI: 0.73, 0.90; P P P Conclusion In cirrhotic patients, MR radiomic is effective in predicting the presence of chronic HE and in grading its severity.

The estimation of traffic flow variables (flow, space mean speed and density) plays a fundamental role in highways planning and designing, as well as in traffic control strategies. Moving Observer Method (MOM) allows traffic surveys in a road, or in a road network. This paper proposes a novel automated procedure, called MOM-AP based on Moving Observer Method and Digital Image Processing (DIP) Technique able to automatically detect (without human observers) and calculate flow q, space mean speed vs and density k in case of stationary and homogeneous traffic conditions. In order to evaluate how reliable is the MOM-AP, an experiment has been carried out in a segment of one two-lane single carriageway road, in Italy. 30 datasets for the segment have been collected (in total 30 round trips). A comparative analysis between MOM-AP and traditional MOM has been carried out. First results show that the current MOM-AP algorithms underestimate the local mean flow variable values of around 10%. Nowadays MOM-AP may be implemented in smartphone apps. Instead, in the near future, it is realistic expecting the increase in the use of automated procedures for calculating the traffic flow variables (based on the “moving observer method”), due to the amount of sensors and digital cameras employed in the new autonomous vehicles (AVs). Considering such technical advances, the MOM-AP is a feasible model for real-time traffic analyses of road networks.

Purpose The purpose of this study was to assess the organization of intrinsic functional brain networks (functional connectome) in neurologically asymptomatic patients with end-stage organ disease who had undergone major surgery for life-threating conditions and compare it to a control group. Materials and Methods Resting-state functional magnetic resonance imaging (rest-fMRI) was performed in 10 adult, post-operative patients with end-stage organ disease. The patients (7 men, 3 women, mean age 57.9 ± 7.4 years) had undergone: n=4 coronary artery bypass graft for heart failure, n=2 orthotopic liver transplantation for non-alcoholic cirrhotic liver failure, n=3 mitral valve repair for heart failure, n=1 pancreaticoduodenectomy for pancreatic papillary tumor. Rest-fMRI was acquired within 48 hours after intensive care unit discharge. Structural brain MR imaging was acquired with T1-weighted, T2-weighted, diffusion weighted imaging, and susceptibility weighted imaging along with 3D isotropic T1-weighted MR images. MR examinations were performed on a 3T MR scanner (Discovery 750w, General Electric Healthcare, Milwaukee, WI, USA). Ten age- and sex-matched healthy controls were studied with the same protocol. Brain functional networks were analyzed by calculating the interregional correlation of low-frequency fluctuations in spontaneous brain activity. Weighted graph-based models were employed to topologically recognize reproducibly determined large-scale functional networks, including default mode, salience, dorsal attention, sensorimotor, visual and language networks using a seed-based approach with Functional Connectivity Toolbox (CONN) (https://web.conn-toolbox.org/) running under MatLab (The MathWorks Inc., Natick, MA, USA). A p-uncorrected < 0.0025 was used to threshold connectomes for voxel-wise paired statistical analysis to take in account the lateralization effect. Network comparisons were thresholded using a false discovery rate (FDR) cluster-level correction approach in patients and controls using the MatLab function “mafdr” for p-uncorrected

Purpose Methods and materials Results Conclusion Personal information and conflict of interest References, Purpose: In the field ofadvancedfunctional magnetic resonance imaging (fMRI) techniques, resting-state functional magnetic resonance imaging (rest- fMRI) represents a novel imaging technique. The task-driven methods (task-fMRI) require a complete patient...

Nowadays safety in railways is mostly achieved by automated system technologies such as ERTMS/ETCS. Nevertheless, on local railways (suburban and regional lines) several tasks still depend on the choices and actions of a human crew. With the aim to improve safety in such type of railways, this research proposes a system for the automatic detection and recognition of railway signs by means of the digital image processing technique. First field applications, carried out on the Italian railway network, show that the proposed system is very accurate (the percentage of correctly detected railway signs is about 97%), even at high train speeds.

The aim of this study was to introduce resting-state functional magnetic resonance imaging (rest-fMRI) capability for brain tumor surgical planning. rest-fMRI is an emerging functional neuroimaging technique potentially able to provide new insights into brain physiology and to provide useful information regarding brain tumors in preoperative and postoperative settings. rest-fMRI evaluates low-frequency fluctuations in the blood oxygen level–dependent signal while the subject is at rest during magnetic resonance imaging examination. Multiple resting-state networks have been identified, including the somatosensory, language, and visual networks, which are of primary importance for surgical planning. We discuss the feasibility of rest-fMRI examination before and after surgical resection of brain tumors in routine clinical practice and the usefulness of the information obtained for surgical planning in brain tumor resection. rest-fMRI is particularly useful for patients who are unable to cooperate with the task-based paradigm, such as children or patients who are sedated, paretic, or aphasic. Although standardization and validation of rest-fMRI are still ongoing, this technique is feasible and valuable and can be implemented for routine clinical surgical planning. © 2019 Elsevier Inc.

This study focuses on both vehicle kinematic parameters (speed and acceleration) and behavior parameters (critical interval and follow-up time) of drivers at turbo-roundabouts. Empirical evaluations of such parameters can be helpful in calibrating traffic microsimulation models or assigning behavior parameters to closed-form capacity models in turbo-roundabouts (gap-acceptance capacity models) and are also related to evaluation of vehicles pollutant emissions. The research was based on traffic process observed in the first turbo-roundabout implemented in the city of Maribor in Slovenia. In 2016 a great number of traffic samples were taken with high-frame-rate video recordings [>50 frames per second (fps)]. All vehicle trajectories were obtained with the methods and algorithms typical of the digital imageprocessing technique (DIP) by filtering the discrete signal of vehicle trajectories f(t)iwith wavelet analysis. The research results showed that vehicle speeds on entry lanes are rather moderate (below 25 km=h, 15 m prior to the Yield line), whereas accelerations usually have values inferior to 2 m=s2on arm lanes and to 1.5 m=s2on ring lanes. The critical intervals tc[distributed according to an Erlang probability distribution function (PDF)] and follow-up headways tf(distributed according to an inverse Gaussian PDF) have, on the other hand, values in ranges of tc= 4.03-5.48 s and tf= 2.52-2.71 s, respectively, according to the right- or left-turn lane and to the major or minor entry in question.

In order to guarantee safety and driving comfort and to maintain an efficient railway infrastructure, the first step is to carefully monitor the track geometry and wear level of the materials constituting the superstructure. To that end diagnostic trains are widely used on main lines, in that they can detect several geometric track parameters and rail wear, but under no circumstances they can yet detect ballast gradation. Due to the practical implications for the planning of maintenance operations on the railway network, this article presents a “DIP” digital image processing technique for measuring the transverse profile and corrugations of the rails as well as ballast gradation. The research was carried out in the laboratory on samples of worn-out rails taken from operational railway lines and in situ in the case of ballast analyses. For the latter, the reliability of the results obtained was assessed by comparison with available results yielded by traditional testing methods. It is shown that the proposed technique can be used not only for laboratory analyses, but most conveniently for high-efficiency in situ surveys, along with the methods traditionally adopted by the railway managing authorities thus contributing to lowering the maintenance cost associated with rail inspection.

The macroscopic mechanical behaviour of dense sands originates to a great extent from their microstructural characteristics such as the coordination number, the grain contact and the packing density indexes. The study of intergranular contacts and the reckoning of these indexes may be carried out on planar images of the grains either by means of time-consuming visual inspection assisted by CAD or by automatic image analysis. An innovative method for the automatic determination of microstructural characteristics of sands based on image analysis of thin section is proposed. Tests performed on a locked sand show that the proposed method is much more effective, convenient and faster than the usual visual inspection technique. Therefore, it can be considered an extremely useful tool for calibrating predictive models for arrangements of granular media based on computer simulations.

The mechanical characteristics of the railway superstructure are related to the properties of the ballast, and especially to the particle size distribution of its grains. Under the constant stress-strain of carriages, the ballast can deteriorate over time, and consequently it should properly be monitored for safety reasons. The equipment which currently monitors the railway superstructure (like the Italian diagnostic train Archimede) do not make any “quantitative” evaluation of the ballast. The aim of this paper is therefore to propose a new methodology for extracting railway ballast particle size distribution by means of the image processing technique. The procedure has been tested on a regularly operating Italian railway line and the results have been compared with those obtained from laboratory experiments, thus assessing how effective is the methodology which could potentially be implemented also in diagnostic trains in the near future.

Road pollutant emissions are correlated mainly to infrastructural capacity and to traffic intensity and typology. With the aim to improve road intersections performances in the last years was designed many new geometric layouts, like turbo roundabouts and flower roundabouts. The main objective of this paper is carried out a comparative analysis between conventional and innovative roundabouts in terms of CO, CO2, NO and PM2,5 vehicular emissions, evaluated by means of COPERT Software which is developed as a European tool for the calculation of emissions from the road transport sector.

The purpose of this paper is to suggest a new procedure for reconstructing the transverse profile of rails in operation by means of image-processing technique. This methodological approach is based on the “information” contained in high-resolution photographic images of tracks and on specific algorithms which allow to obtain the exact geometric profile of the rails and therefore to measure the state of the rail-head extrados wear. The analyses and the results concern rails taken from railway lines under upgrading by means of mono- and stereoscopic methods which are appropriate to be employed in laboratory applications or in high-efficiency surveys in situ.

An image analysis technique for automatic traffic sign detection and classification is proposed. This analysis makes it possible, after proper training, to detect, recognize and classify vertical road signs from video frames acquired on a moving vehicle equipped with cameras, as well as to identify anomalies with respect to road sign regulations (positioning and visibility). The experimental results show that this technique allows one to correctly detect and classify almost all vertical signs and, mainly in extra-urban environment, it can be considered as highly reliable, apart from being really versatile and user-friendly for road inventory and road maintenance purposes.

The mechanical properties of bituminous mixture strongly depend on the gradation of the aggregate that represents the mineral skeleton of the mixture, since for open and gap-graded mixtures, stresses due to vehicles in movement on the pavement are mainly transmitted through their contacts. Internal structure of bituminous mixture is, therefore, of great interest for road and infrastructure engineering and it is appropriate to study it with recently developed image analysis method. The purpose of this study was to finalize an effective analysis of asphalt section image for automatically extracting aggregate gradation without the need of separation of the bitumen from the aggregate. This paper proves that, thanks to the synergic use of different segmentation methods of the digital images taken on slices of cores from the pavement, it is possible to obtain a reliable gradation of the mineral skeleton of the mixture. The proposed methodology allows one to estimate the aggregate gradation that, otherwise, it would be necessary to establish via specially equipped laboratory, undertaking time-consuming tests that also imply health risks for the operators, due to the use of solvents and other hazardous materials.

Monitoring the wear condition of the tramway superstructure is one of the key points to guarantee an adequate safety level of the light rail transport system. The purpose of this paper is to suggest a new non-conventionalprocedure for measuring the transverse profile of rails in operation by means of image-processing technique. This methodological approach is based on the “information” contained in high-resolution photographic images of tracks and on specific algorithms which allow to obtain the exact geometric profile of the rails and therefore to measure the state of the rail-head extrados wear.